An alternate (and very nerdy) theory to the war of five kings

For fans of HBO’s Game of Thrones, it is commonly held that the controversy over royal succession, following the regicide of King Robert, was the main impetus for the series of wars that later ravaged Westeros. While this certainly fueled tensions amongst the lords with a claim to the iron throne, I believe that there was an underlying loss of confidence in the economy that: (1) led knowledgable insiders to believe that regicide was an optimal strategy, (2) reduced the benefits of union and decreased the cost for houses to rebel, and (3) created a social instability that made commoners more open to political upheaval.

The king's excesses require an ever larger horse

King Robert's economic policies

King Robert, like many medieval chieftains, was a profligate spender who indulged in the lavish and whimsically excessive. He had assumed the throne in King’s Landing at a time of budget surplus and ample gold reserves. However, by the time the show catches up to Robert, he had wasted away his treasure and was financing his vices through debt and dubious accounting.

As a result of the financial mastery of Little Finger, revenues had increased about ten fold during King Robert’s reign. While we don’t know for sure, it is safe to say that the increased revenue was neither a result of exemplarily economic growth nor increased bureaucratic efficiency in tax collection. It is therefore likely that the resultant cash windfall was the result of higher taxes—and of course, debt. With winter coming and crop yields falling in the North, along with a likely fixed-sum tax (I assume a medieval society lacked the technical proficiency for progressive tax collection—i.e. a farmer owed X barrels of hay rather than X% of his production), the higher real rate of taxation would have lead to an increasing and disproportionate financial burden across the seven kingdoms (a farmer in the North was producing Y and being taxed X. In winter, he produces Y – W and is taxed X). Along with King Robert’s reckless fiscal policy, the monetary situation made things doubly bleak. We know that the Lannister gold mines had run dry and the economy was being flooded with creditor’s cash. It is a safe bet that Tywin Lannister was busy debasing the gold supply (diluting the amount of gold in a given coin)—to maintain the appearance of ample gold reserves and news of his exhausted mines quiet—and as a result destroying the currency as a unit of account in the marketplace—new coins are worth less than old coins because they contain less gold, and consumers, because of money illusion (people think of prices/wages in nominal rather than real terms), experience great difficulty in pricing goods and labor appropriately in the short run.

Inflation: it looks the same, but is worth less and less.

The increased government demand for resources (for the lavish festivities, etc.), financed by debt and high taxation, and backed by a depleted gold supply, was likely driving up consumer prices and deflating the exchange value of the currency—making goods ever more expensive at home and cheap foreign alternatives ever more expensive to import.

Stagflation in Westeros

Stagflation occurs when prices are going up and economic activity is going down. This is the dire situation likely faced in Westeros in the run-up to the War of the Five Kings.

Why was economic activity likely going down? In feudal societies, the vast majority of workers are agrarian. Without increases in productivity, and we are aware of none on Westeros, farmworkers produce an amount that is constrained by their hours of labor and the weather conditions. Due to the coming of winter, we can assume that crop yields were down. We also know that farmers needed to give a fixed amount of their yield to their local lord. As farmers retained less of the yield of their production as profit, they would become less incentivized to supply their labor—as the opportunity cost of leisure was lessened by the substitution effect (keeping less of what you produce is in effect a decrease in wage, making work less attractive—within a set range of incomes). This would compound the weather’s effect on crop yields and lead to a dramatic reduction in economic activity.

To make matters worse, the gold-backed currency was being debased to accommodate government spending. Why does this matter? With less being produced on the farms, the increase in money supply couldn't possibly be representing the underlying economic activity (there was too much money for too few crops!). This means that the price of goods was rising (inflation) and because people weren’t producing as much, their real (rather than nominal) wages were decreasing. To add to the misery, exchange in the cities would also likely be decreasing. The debasement of the currency would have led to arbitrary income redistribution across local debtors and creditors (old nominal debts can be paid back with “cheaper” new money) and made lenders increase the interest on their new loans— further diminishing economic activity through reduced investment. Inflation would have also made trading more complex as merchants lost faith in the value of the currency (how would the blacksmith set the value of a sword if he doesn’t know the value of the currency?). In all likelihood, merchants would resort to a barter economy, reducing taxable income, and further inflaming the fiscal crisis in King’s Landing. All in all, a dire situation that to any observant lord was unsustainable.

Calculating Revolt

With farmers and merchants squeezed by falling output, increased prices, and increased taxes, the tension on the streets would have been palpable. In calculating the decision to revolt— beyond those who had a personal vanity in assuming their place on the throne—lords would have considered the following factors in deciding whether to go to war:

SWOT analysis for the decision to revolt

Excluding the houses with a personal claim to the throne, which have a different calculus, the SWOT analysis, in my opinion, favors removing King Robert, and after his removal, going to war with the Lannisters. Why would the Lannisters initiate the removal of King Robert and invite the likely rebellion that would follow? First-mover advantage. Sensing the impending economic disaster, and their own complicity as house of the queen, the Lannisters calculated that their best chance at continued survival was to remove King Robert and seize the levers of power (strategic bridges, city guard, royal fleet, etc.) before an opponent could— providing them with a strategic advantage.

Of the actors nominated for ‘Best Actor-leading role’ at the Oscars— the premier international awards for film— I was curious to learn more about their measurable attributes (age, national origin, film credits), the commercial success of the film they starred in, and how these variables might or might not be correlated with ultimately winning the award. The Oscars are nothing without the self-reverence and controversy they inspire. For example, my favorite performance from 2014— Jake Gyllenhaal in Nightcrawler— was likely snubbed, not as a result of his exemplary psychopathic depiction, but as a function of the odd voting regime and the film’s overall ‘momentum’. Even after receiving a nomination, there is substantial cynicism surrounding the underlying biases that seem to dictate winners. For example, I assumed that actors from commercially successful films were disadvantaged— the academy (the Oscars’ voting body) deeming popularity the equivalent of lowbrow and thus unworthy of acting excellence.

Who gets nominated?

I created a dataset of the Best Actor nominees from 2000-2014, using IMDB, that included the following variables: (1) the actor’s age at the time of the film, (2) the actor's number of previous feature film acting credits, (3) the most up-to-date gross box office revenue in the United States- adjusted for inflation to 2000 US dollars (4) whether they won the oscar, (5) whether they were American, and (6) whether they had previously won for best actor (supporting or lead). This added up to 75 observational units and 450 data points.

The descriptive statistics were as follows:

The winners are shockingly representative of the overall pool of nominees. Immediately, it seemed clear that it would be incredibly unlikely for any of these variables to be a statistically valid indicator of the likelihood of winning the award. While the two groupings had very similar averages, the standard deviations—a measurement of the clustering of observations around the mean— for all nominees were larger than for the winners. For example, the standard deviation for the winner’s age was 7.6 years versus 12.2 for all nominees and the standard deviation for box office revenue was $50.5 million for the winners versus $58.8 million for all nominees.

Why does this matter? It might imply that while the academy is open to nominating a wide range of actors representing a variety of films (a large standard deviation), the winners are more likely to fit an archetype (a small standard deviation). Here is a look at a chart of the inflation adjusted box office revenue of the winners and those nominated:

The three largest revenue films were, in order of highest revenue: Pirates of the Caribbean (Depp, 2003), American Sniper (Cooper, 2014), and Cast Away (Hanks, 2000). The actor from the highest revenue film to win was Russell Crowe for Gladiator (2000), which earned $187.7 million in domestic box office. The lowest grossing film to earn a nomination for best actor was A Better Life (Bichir, 2011), which earned a paltry $1.3 million in the U.S. The trend line—the black dash cutting through the chart— shows that while there is variation from year-to-year, the revenues have remained fairly constant in real terms. And of course, as I mentioned before, the winners are more closely clustered around the trend line. Now for a look at the actor’s ages:

Here, the trend line indicates that the nominees have gotten slightly older over time, despite Eddie Redmayne winning for the Theory of Everything in 2014 at age 32. The oldest winner was Jeff Bridges (Crazy Heart, 2009) at age 60. The youngest nominees were Ryan Gosling (Half Nelson, 2006) and Heath Ledger (Brokeback Mountain, 2005) both at age 26. The oldest was Bruce Dern (Nebraska, 2013) at age 77.

Intriguingly, there also appears to be a relationship between age and box office (I highlighted the three outliers, highest grossing films, in red to emphasize their impact on the trend line):

What can be said statistically?

As I assumed, after examining the similarities between the two group’s means, none of the variables, after many attempted transformations, functional form specifications, and regression models, were a statistically significant indicator of winning for best actor. The model below reflects many failed attempts to reject a non-zero relationship— non-zero implying that a change in the predictor variable is correlated with a change in the dependent variable.

Here the statistical significance of the beta on natural log of box office revenue (I used natural log to make the relationship linear, although you can ignore this for our purposes) is rejected at the 10% significant level— basically, there is no reason to interpret it. However, where I did find some interesting correlations was conducting a log-linear regression of inflation-adjusted box office revenue on age, nationality, and winning the Oscar. Looking first at a model of Log(Box Office^) = Age^:

Rejecting the null hypothesis that there is no relationship at the 10% level (statistical speak), increasing the age of the lead actor by one year is, on average, associated with a 2.1% decrease in box office revenue. This result should be taken with a grain of salt, however, as the 95% confidence interval does include 0 (although just barely) and it likely suffers from severe omitted variable bias—which would invalidate these results. For example, older lead actors might garner a lower production budget from the studio, which would be correlated with both their age and the film’s box office revenue. Luckily, I have a couple more variables that I can include. Could the lead actor being American also be influencing the film’s revenue? Could the relative quality of the performance (winning the Oscar) be influencing revenue?

Controlling for the relative acting performances (whether it won the Oscar) and a possible home country bias (whether the actor is American), does lead to a slightly more accurate model. Age is now statistically significant at the 5% level and its 95% confidence interval no longer includes 0. Not surprisingly, and as we saw from the difference in film revenues earlier, whether the film won for best actor is not statistically significant. However, if the lead actor is American could potentially be influencing the box office revenues— it is statistically significant at the 10% level and suggests that an American lead actor is associated, on average, with a 50% increase in box office revenues (from the descriptive data, the average box office revenue for an American is $60 million versus $54.7 million for a non-American). Again though, I would urge caution, as there is likely serious omitted variable bias in this result. For example, whether the film is produced in America, as opposed to internationally, could be correlated with whether the lead actor is American and box office revenue.

Out of the 115.6 million homes with televisions in 2014, approximately 7.8 million tuned in for The Bachelor season 19 premier- 7% of all TV viewership at 8pm EST that Monday night.﻿ Of these 7.8 million, it is likely that a sizable portion of loyal fans were gambling on the show’s outcome- as measured by a contestant’s longevity before being eliminated based on the contestant’s biographical data (which on the ABC homepage includes a photo, their age, occupation, and hometown). Whether the gambling was formal- there exist a number of online sports books that allow for betting on reality television despite information that can leak prior to the show airing- or informal- pools and drafts that may or may not include a financial reward- the show has potentially produced enough data over its 19 seasons for a savvy gambler to position him/herself statistically. Given the data available on the contestants before the season premier, and accounting for attractiveness not being readily or objectively quantifiable, an analysis can be performed that looks at the week a contestant was eliminated versus their age, hometown, and occupation. The data was compiled for seasons 11- 19 and included 239 contestants. A contestant’s outcome was measured to be the week they were eliminated, with a higher number week being a better outcome than a lower one. Elimination weeks ranged from 1 to 11 (for seasons 15-19) and 1 to 9 (for seasons 11-15) with the winner being coded ‘week 11’ (or ‘week 9’) and the runner-up being coded ‘week 10’ (or ‘week 8’). Age required no coding and was entered directly from the biographical data. The average age was 26.7 years with a high of 36 and a low of 21.

For occupation, I decided to create a binary variable that would be equal to 1 if the contestant’s job required a college degree and 0 if it did not. If it was not obvious that the listed position required a college degree (i.e. ‘account manager’) then it was coded as a 0. This required some judgment on my part and it is possible contestants are incorrectly coded. Overall, 86 out of 238 (36%) contestants were coded as having a college degree. For hometown, I decided to create a binary variable that would provide a proxy for ‘Southern’. I coded the hometown as 1 if the contestant was from a state that voted for Mitt Romney in the 2012 presidential campaign and as 0 if they were from a state that voted for Barack Obama- international contestants were coded as 0. Overall, 68 out of 238 (29%) contestants originated from a state that supported Romney in 2012. Next, I wanted to use regression analysis to see how these variables (age, hometown, and occupation) were associated with the number of weeks a contestant remained on the show. Of course, there are many other omitted variables that impact longevity and the choice of ‘bachelor’ impacts the desired attributes of a contestant. This analysis is merely designed to identify past correlations. First, I ran a regression of age on elimination week:

While the coefficient on age is negative (meaning that as age increases their longevity or week eliminated decreases), it is not statistically significant- even at the 10% level. This means that it is likely that if the true relationship between age and elimination week were 0 (i.e. they are not related) we would find an effect as large as the one we found 19.2% of the time. We therefore fail to reject this null hypothesis and can’t conclude that age impacts a contestant’s longevity on the show.

Second, I ran a regression of occupation on elimination week:

Again, while the coefficient on occupation is negative, it is not statistically significant at the 10% level- in fact this time it is not even close. This means that while there is a very loose negative correlation between holding a college degree and elimination week, it can’t be distinguished from a purely random result. Third, I ran a regression of hometown on elimination week:

In this regression, there is a statistically significant positive relationship at the 1% level. What does this mean? It means that if being from a state that voted for Mitt Romney had no real impact on your elimination week, we would only find a result as large as what we found in 0.006% of samples. This means we can conclude the following: being from a state that voted for Mitt Romney in 2012 is associated on average with remaining on the Bachelor for an additional 1.085 weeks.

So while being older and educated may not have a true negative relationship with contestant longevity, it appears to have been a very good bet to choose contestants from politically conservative states over the past 8 seasons- and to continue choosing them in the future.